This is an R Markdown Notebook with the analysis of the NICE online study. To run the code below, you can open the file in RStudio.

Load libraries

library(furniture) # formating table 1
library(tidyverse) # general wranling
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Load data

Study data

## [1] 377
##  [1] "Video"                    "Restoration"             
##  [3] "Presence"                 "WillingnessToWalk"       
##  [5] "Beauty"                   "Structure"               
##  [7] "Interest"                 "Familiarity"             
##  [9] "Scenic"                   "Crowdedness"             
## [11] "Width"                    "Valence"                 
## [13] "Arousal"                  "trial_duration"          
## [15] "Distracted_Time"          "SSA_Mean"                
## [17] "Concern_Covid_Mean"       "SIAS_Total_Mean"         
## [19] "SPS_Total_Mean"           "ipip_Extroversion"       
## [21] "ipip_Agreeableness"       "ipip_Conscientiousness"  
## [23] "ipip_Neuroticism"         "ipip_Openness"           
## [25] "ipip_Honesty"             "Crowd_Preference_Mean"   
## [27] "Age"                      "Background_Architecture" 
## [29] "Background_Arts"          "Background_Rural"        
## [31] "Grow.up.country"          "Grow.up.City"            
## [33] "Lives_now.x"              "years_Lives_now.x"       
## [35] "NSS"                      "Condition"               
## [37] "screen_width"             "screen_height"           
## [39] "video_name"               "video_name_df"           
## [41] "video_name_df_keep"       "video_quality"           
## [43] "video_rendition"          "video_fps"               
## [45] "video_duration"           "video_Country"           
## [47] "video_Cluster"            "video_City"              
## [49] "video_testing_group"      "video_primary_category"  
## [51] "video_secondary_category" "video_n_frames"          
## [53] "video_mean_pedcounts"     "video_max_pedcounts"     
## [55] "video_sum_pedcounts"      "Distracted_n"            
## [57] "Buffering"                "Sex"                     
## [59] "Language"                 "ArtsBg"                  
## [61] "ArchBg"                   "Lives_now.y"             
## [63] "years_Lives_now.y"        "urban_rural_background"  
## [65] "urban_rural_now"          "Employment_status"       
## [67] "Nationality"              "Approval_rate"           
## [69] "Ethnicity"                "Student_status"          
## [71] "Employment"               "Age_breaks"              
## [73] "Education"                "Upbringing_Environment"  
## [75] "Current_Environment"      "Anon_ID"

Participant characteristics

This table shows participants’ demographic characteristics. It will be transferred to Latex and to the manuscript at a later stage.

Note: “From prolific.co https://researcher-help.prolific.co/hc/en-gb/articles/360019238413-Representative-samples-FAQ:

Starting with the youngest allowable participation age on Prolific, we stratify age using five 9-year brackets: 18-27, 28-37, 38-47, 48-57, and 58+. ‘Sex’ is stratified into male and female. ‘Ethnicity (simplified)’ is stratified into the five categories recommended by the UK Office of National Statistics: White, Mixed, Asian, Black and Other.”

## 
##  Participant characteristics
## 
## ──────────────────────────────────────────────────────────────────────────
##                                                          Sex 
##                                       Total       Female      Male       
##                                       n = 377     n = 186     n = 191    
##  Age                                                                     
##     18–29                             229 (60.7%) 112 (60.2%) 117 (61.3%)
##     30-49                             125 (33.2%) 61 (32.8%)  64 (33.5%) 
##     50-64                             22 (5.8%)   12 (6.5%)   10 (5.2%)  
##     65+                               1 (0.3%)    1 (0.5%)    0 (0%)     
##  Education                                                               
##     Architecture                      17 (4.5%)   9 (4.8%)    8 (4.2%)   
##     Arts                              50 (13.3%)  26 (14%)    24 (12.6%) 
##     Other                             310 (82.2%) 151 (81.2%) 159 (83.2%)
##  Ethnicity                                                               
##     Asian                             31 (8.2%)   15 (8.1%)   16 (8.4%)  
##     Black                             54 (14.3%)  33 (17.7%)  21 (11%)   
##     Mixed                             55 (14.6%)  28 (15.1%)  27 (14.1%) 
##     Other                             24 (6.4%)   10 (5.4%)   14 (7.3%)  
##     White                             213 (56.5%) 100 (53.8%) 113 (59.2%)
##  Employment                                                              
##     Other (e.g., unemployed, retired) 209 (55.4%) 106 (57%)   103 (53.9%)
##     Working/Studying                  168 (44.6%) 80 (43%)    88 (46.1%) 
##  Upbringing Environment                                                  
##     Other                             189 (50.1%) 86 (46.2%)  103 (53.9%)
##     Urban                             188 (49.9%) 100 (53.8%) 88 (46.1%) 
##  Current Environment                                                     
##     Other                             126 (33.4%) 62 (33.3%)  64 (33.5%) 
##     Urban                             251 (66.6%) 124 (66.7%) 127 (66.5%)
##  Approval rate                                                           
##                                       99.5 (1.0)  99.5 (1.0)  99.5 (1.0) 
## ──────────────────────────────────────────────────────────────────────────